Echo integration is a well-established method for estimating fish biomass, but is challenging for a low target strength (TS), deep-living fish species such as orange roughy (Hoplostethus atlanticus). A novel approach has been to utilize the infrastructure of the fishing vessel's trawlnet by attaching an acoustic-optical system (AOS) to the net's headline. Deep deployment of the AOS via the trawlnet reduces uncertainties associated with hull-mounted acoustics that include the influence of weather on data quality, low resolution due to the long range to target, large acoustic dead zone on sloping seabed, and inability to identify and differentiate other fish that co-occur with the target species. The AOS system simultaneously records acoustic data at multiple frequencies (38 and 120 kHz), species composition in video and stereo imagery, and environmental data as the net collects biological samples and/or commercial catch. All data streams were considered in a multiple-lines-of-evidence approach to give improved estimates of orange roughy biomass with low error due to species uncertainty. AOS-based biomass estimates, made over a 5-year period from 11 key spawning locations in Australia and New Zealand, showed a strong correlation (r² = 0.97, n = 39) between frequencies (38 and 120 kHz); the 38 kHz estimates were, on average, 8% higher than 120 kHz, with a standard deviation of 20%. This similarity in estimates across frequencies improves confidence in results compared with single-frequency surveys that are potentially prone to large errors resulting from unknown (mixed) species composition and target strengths, calibration, and sound absorption uncertainties.

Item Type:	Refereed Article
Keywords:	acoustics, biomass, deepwater, multifrequency, optics, orange roughy
Research Division:	Agricultural, Veterinary and Food Sciences
Research Group:	Fisheries sciences
Research Field:	Fish physiology and genetics
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the biological sciences
UTAS Author:	Kloser, RJ (Dr Rudy Kloser)
ID Code:	118323
Year Published:	2016
Web of Science® Times Cited:	5
Deposited By:	Directorate
Deposited On:	2017-07-10
Last Modified:	2018-05-10
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